Model-data integration and network design for biogeochemical research: an NCAR-CSU summer school (original) (raw)

Data assimilation: a powerful tool for atmospheric chemistry

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1999

Issues such as ozone depletion, acid rain and photochemical smog are all of considerable environmental importance. These issues are studied using the dual approach of observations and numerical modelling. In making balanced assessments of these issues it is vital to make the best use of all the information available to us, both theoretical and observational. This is a non-trivial task. The technique of 'data assimilation' is a powerful tool that allows us to address this issue. It is revolutionizing the way we can study atmospheric chemistry. Data assimilation allows us to simultaneously make good use of however many observations are available to us, our theoretical understanding, and any a priori information we have, within a mathematical framework. It even allows us to infer information about chemical constituents that are not observed. As we move into the new millennium it is a technique that is set to grow rapidly in importance.

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Geoscientific Model Development, 2021

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistrytransport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs Published by Copernicus Publications on behalf of the European Geosciences Union. 5332 A. Berchet et al.: The Community Inversion Framework and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Process Discovery through Assimilation of Complex Biogeochemical Datasets

2021

This white paper addresses two of three focal areas identified in the white paper call: 1) Biogeochemical data acquisition and assimilation enabled by machine learning and 3) Insight gleaned from complex data using AI. We focus on AI application to complex biogeochemistry (BGC) data (e.g. laboratory experimental data, field manipulation data, literature data), which is an untapped source of information for improving Earth System Predictability (ESP). Science Challenge Laboratory experiments and field manipulation experiments are critical to interrogate the impact of hydrological and climate perturbations on BGC processes in a controlled environment. Hydrologicallydriven BGC processes are a key aspect of ESP, particularly at dynamic interfaces (e.g. terrestrial-aquatic interfaces, hot spots/hot moments), because BGC processes govern the cycling of nutrients, metals, and organic matter. However, BGC experiments yield a complex array of data types (spatiotemporal observational and laboratory measurements, microscopy image data, spectroscopy data, etc.), which hampers assimilation and analysis, as well as the application of machine learning (ML). Ensuring that these data are findable, accessible, interoperable, and reusable (FAIR) is of paramount importance. AI/ML methods are poised to transform the way we incorporate complex BGC laboratory and field manipulation experimental data into earth and environmental systems models, quantify data uncertainty, design future experiments, and develop new models with unprecedented fidelity and resolution. 1

A data-centered collaboration portal to support global carbon-flux analysis

Concurrency and Computation: Practice and Experience, 2010

Carbon-climate, like other environmental sciences, has been changing. Large-scale synthesis studies are becoming more common. These synthesis studies are often conducted by science teams that are geographically distributed and on datasets that are global in scale. A broad array of collaboration and data analytics tools are now available that could support these science teams. However, building tools that scientists actually use is hard. Also, moving scientists from an informal collaboration structure to one mediated by technology often exposes inconsistencies in the understanding of the rules of engagement between collaborators. We have developed a scientific collaboration portal, called fluxdata.org, which serves the community of scientists providing and analyzing the global FLUXNET carbon-flux synthesis dataset. Key things we learned or re-learned during our portal development include: minimize the barrier to entry, provide features on a just-in-time basis, development of requirements is an on-going process, provide incentives to change leaders and leverage the opportunity they represent, automate as much as possible, and you can only learn how to make it better if people depend on it enough to give you feedback. In addition, we also learned that splitting the portal roles between scientists and computer scientists improved user adoption and trust. The fluxdata.org portal has now been in operation for ~;;1.5 years and has become central to the FLUXNET synthesis efforts.

Towards an online-coupled chemistry-climate model: evaluation of COSMO-ART

Geoscientific Model Development Discussions, 2011

The online-coupled, regional chemistry transport model COSMO-ART is evaluated for periods in all seasons against several measurement datasets to assess its ability to represent gaseous pollutants and ambient aerosol characteristics over the European domain. Measurements used in the comparison include long-term station observations, satellite and ground-based remote sensing products, and complex datasets of aerosol chemical composition and number size distribution from recent field campaigns. This is the first time these comprehensive measurements of aerosol characteristics in Europe are used to evaluate a regional chemistry transport model. We show a detailed analysis of the simulated size-resolved chemical composition under different meteorological conditions. The model is able to represent trace gas concentrations with good accuracy and reproduces bulk aerosol properties rather well though with a clear tendency to underestimate both total mass (PM10 and PM2.5) and aerosol optical depth. We find indications of an overestimation of shipping emissions. Time evolution of aerosol chemical composition is captured, although some biases are found in relative composition. Nitrate aerosol components are on average overestimated, and sulfates underestimated. The accuracy of simulated organics depends strongly on season and location. While strongly underestimated during summer, organic mass is comparable in spring and autumn. We see indications for an overestimated fractional contribution of primary organic matter in urban areas and an underestimation of SOA at many locations. Aerosol number concentrations can be simulated well, size distributions are comparable. Our work sets the basis for subsequent studies of aerosol characteristics and climate impacts with COSMO-ART, and highlights areas where improvements are necessary for current regional modeling systems in general.

A global interactive chemistry and climate model: Formulation and testing

Journal of Geophysical Research, 1998

In order to elucidate interactions between climate change and biogeochemical processes and to provide a tool for comprehensive analysis of sensitivity, uncertainty, and proposed climate change mitigation policies, we have developed a zonally averaged two-dimensional model including coupled biogeochemical and climate sub-models, as a part of an Integrated Global System Model. When driven with calculated or estimated trace gas emissions from both anthropogenic and natural sources, it is designed to simulate centennial-scale evolution of many radiatively and chemically important tracers in the atmosphere. Predicted concentrations of chemical species in the chemistry sub-model are used interactively to calculate radiative forcing in the climate sub-model which in turn provides winds, temperatures, and other variables to the chemistry sub-model. Model predictions of the surface trends of several key species are close to observations over the past 10-20 years. Predicted vertical distributions of climate-relevant species, as well as seasonal variations, are also in good agreement with observations. Runs of the model imply that, if the current increasing trends of anthropogenic emissions of climate-relevant gases are continued over the next century, the chemical composition of the atmosphere would be quite different in the year 2100 than that currently observed. The differences involve not only higher concentrations of major long-lived trace gases such as CO 2 , N 2 O, and CH 4 , but also about 20% lower concentrations of the major tropospheric oxidizer (OH free radical), and almost double the current concentrations of the short-lived air pollutants CO and NO x .